The present invention relates generally to modifying a language conversation model, and more specifically, to detecting errors within one or more human-computer conversations and utilizing machine learning to develop a language conversation model modification that addresses the detected errors.
Contemporary voice service systems allow a person (e.g., a customer) to verbally interact with a computer, such as when a person places a telephone call to a helpdesk line and/or a customer service center. Contemporary voice service systems are unable to process verbal inputs that do not fit a discrete input format. For example, if the input format requires receiving a name input as the first name followed by the last name, it may not be able to process an input provided as the last name followed by the first name. When contemporary voice service systems receive verbal inputs that do not fit their discrete input format, the human-computer interaction is stalled, which often results in the customer becoming frustrated and terminating the telephone call before the issue that prompted the call has been addressed.